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Declarative Machine Learning
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package org.apache.sysml.runtime.controlprogram.parfor;
import java.util.Collection;
import org.apache.sysml.hops.OptimizerUtils;
import org.apache.sysml.runtime.controlprogram.context.SparkExecutionContext;
import org.apache.sysml.runtime.controlprogram.parfor.stat.Stat;
import org.apache.sysml.runtime.controlprogram.parfor.stat.StatisticMonitor;
import org.apache.sysml.runtime.controlprogram.parfor.stat.Timing;
/**
* Instances of this class can be used to execute tasks in parallel. Within each ParWorker
* multiple iterations of a single task and subsequent tasks are executed sequentially.
*
* Resiliency approach: retry on computation error, abort on task queue error
*
*
*/
public class LocalParWorker extends ParWorker implements Runnable
{
protected LocalTaskQueue _taskQueue = null;
protected Collection _fnNames = null;
protected boolean _stopped = false;
protected int _max_retry = -1;
public LocalParWorker( long ID, LocalTaskQueue q, ParForBody body, int max_retry, boolean monitor )
{
super(ID, body, monitor);
_taskQueue = q;
_stopped = false;
_max_retry = max_retry;
}
/**
* Sets the status to stopped such that execution will be aborted as soon as the
* current task is finished.
*/
public void setStopped() {
_stopped = true;
}
public void setFunctionNames(Collection fnNames) {
_fnNames = fnNames;
}
public Collection getFunctionNames() {
return _fnNames;
}
@Override
public void run()
{
// monitoring start
Timing time1 = ( _monitor ? new Timing(true) : null );
//setup fair scheduler pool for worker thread
if( OptimizerUtils.isSparkExecutionMode() ) {
SparkExecutionContext sec = (SparkExecutionContext)_ec;
sec.setThreadLocalSchedulerPool("parforPool"+_workerID);
}
// continuous execution (execute tasks until (1) stopped or (2) no more tasks)
Task lTask = null;
while( !_stopped )
{
//dequeue the next task (abort on NO_MORE_TASKS or error)
try
{
lTask = _taskQueue.dequeueTask();
if( lTask == LocalTaskQueue.NO_MORE_TASKS ) // task queue closed (no more tasks)
break; //normal end of parallel worker
}
catch(Exception ex)
{
// abort on taskqueue error
LOG.warn("Error reading from task queue: "+ex.getMessage());
LOG.warn("Stopping LocalParWorker.");
break; //no exception thrown to prevent blocking on join
}
//execute the task sequentially (re-try on error)
boolean success = false;
int retrys = _max_retry;
while( !success )
{
try
{
///////
//core execution (see ParWorker)
executeTask( lTask );
success = true;
}
catch (Exception ex)
{
LOG.error("Failed to execute "+lTask.toString()+", retry:"+retrys, ex);
if( retrys > 0 )
retrys--; //retry on task error
else
{
// abort on no remaining retrys
LOG.error("Error executing task: ",ex);
LOG.error("Stopping LocalParWorker.");
break; //no exception thrown to prevent blocking on join
}
}
}
}
//setup fair scheduler pool for worker thread
if( OptimizerUtils.isSparkExecutionMode() ) {
SparkExecutionContext sec = (SparkExecutionContext)_ec;
sec.cleanupThreadLocalSchedulerPool();
}
if( _monitor ) {
StatisticMonitor.putPWStat(_workerID, Stat.PARWRK_NUMTASKS, _numTasks);
StatisticMonitor.putPWStat(_workerID, Stat.PARWRK_NUMITERS, _numIters);
StatisticMonitor.putPWStat(_workerID, Stat.PARWRK_EXEC_T, time1.stop());
}
}
}